SoroushVahidi/scidocs huggingface dataset:
Bibliometric information retrieval system (BIRS): A web search interface utilizing bibliometric research results
(2000)

The aim of this article is to test whether the results obtained from a specific bibliographic research can be applied to a real search environment and enhance the level of utility of an information retrieval session for all levels of end users. In this respect, a Web-based Bibliometric Information Retrieval System (BIRS) has been designed and created, with facilities to assist the end users to get better understanding of their search domain, formulate and expand their search queries, and visualize the bibliographic research results. There are three specific features in the system design of the BIRS: the information visualization feature of the BIRS (cocitation maps) to guide the end users to identify the important research groups and capture the detailed information about the intellectual structure of the search domain; the multilevel browsing feature to allow the end users to go to different levels of interesting topics; and the common user interface feature to enable the end users to search all kinds of databases regardless of different searching systems, different working platforms, different database producer and supplier, such as different Web search engines, different library OPACs, or different on-line databases. A preliminary user evaluation study of BIRS revealed that users generally found it easy to form and expand their queries, and that BIRS helped them acquire useful background information about the search domain. They also pointed out aspects of information visualization, multilevel browsing, and common user interface as novel characteristics exhibited by BIRS.
Elementary Siphons of Petri Nets and Deadlock Control in FMS
(2015)

For responsiveness, in the Petri nets theory framework deadlock prevention policies based elementary siphons control are often utilized to deal with deadlocks caused by the sharing of resources in flexible manufacturing system (FMS) which is developing the theory of efficient strict minimal siphons of an S3PR. Analyzer of Petri net models and their P-invariant analysis, and deadlock control are presented as tools for modelling, efficiency structure analysis, control, and investigation of the FMSs when different policies can be implemented for the deadlock prevention. We are to show an effective deadlock prevention policy of a special class of Petri nets namely elementary siphons. As well, both structural analysis and reachability graph analysis and simulation are used for analysis and control of Petri nets. This work is successfully applied Petri nets to deadlock analysis using the concept of elementary siphons, for design of supervisors of some supervisory control problems of FMS and simulation of Petri net tool with MATLAB.
Omega-3 Fatty Acids and Inflammatory Processes
(2010)

Long chain fatty acids influence inflammation through a variety of mechanisms; many of these are mediated by, or at least associated with, changes in fatty acid composition of cell membranes. Changes in these compositions can modify membrane fluidity, cell signaling leading to altered gene expression, and the pattern of lipid mediator production. Cell involved in the inflammatory response are typically rich in the n-6 fatty acid arachidonic acid, but the contents of arachidonic acid and of the n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can be altered through oral administration of EPA and DHA. Eicosanoids produced from arachidonic acid have roles in inflammation. EPA also gives rise to eicosanoids and these often have differing properties from those of arachidonic acid-derived eicosanoids. EPA and DHA give rise to newly discovered resolvins which are anti-inflammatory and inflammation resolving. Increased membrane content of EPA and DHA (and decreased arachidonic acid content) results in a changed pattern of production of eicosanoids and resolvins. Changing the fatty acid composition of cells involved in the inflammatory response also affects production of peptide mediators of inflammation (adhesion molecules, cytokines etc.). Thus, the fatty acid composition of cells involved in the inflammatory response influences their function; the contents of arachidonic acid, EPA and DHA appear to be especially important. The anti-inflammatory effects of marine n-3 PUFAs suggest that they may be useful as therapeutic agents in disorders with an inflammatory component.
Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics.
(2017)

The renewed effort to eliminate malaria and permanently remove its tremendous burden highlights questions of what combination of tools would be sufficient in various settings and what new tools need to be developed. Gene drive mosquitoes constitute a promising set of tools, with multiple different possible approaches including population replacement with introduced genes limiting malaria transmission, driving-Y chromosomes to collapse a mosquito population, and gene drive disrupting a fertility gene and thereby achieving population suppression or collapse. Each of these approaches has had recent success and advances under laboratory conditions, raising the urgency for understanding how each could be deployed in the real world and the potential impacts of each. New analyses are needed as existing models of gene drive primarily focus on nonseasonal or nonspatial dynamics. We use a mechanistic, spatially explicit, stochastic, individual-based mathematical model to simulate each gene drive approach in a variety of sub-Saharan African settings. Each approach exhibits a broad region of gene construct parameter space with successful elimination of malaria transmission due to the targeted vector species. The introduction of realistic seasonality in vector population dynamics facilitates gene drive success compared with nonseasonal analyses. Spatial simulations illustrate constraints on release timing, frequency, and spatial density in the most challenging settings for construct success. Within its parameter space for success, each gene drive approach provides a tool for malaria elimination unlike anything presently available. Provided potential barriers to success are surmounted, each achieves high efficacy at reducing transmission potential and lower delivery requirements in logistically challenged settings.
Accurate design of corrugated waveguide low-pass filters using exclusively closed-form expressions
(2017)

A simple, quasi-analytical, and accurate method to design classical corrugated rectangular waveguide low-pass filters is presented in this paper. Unlike in other published techniques, the dimensions of the final filter are directly obtained in a quasi-analytical manner following exclusively closed-form expressions and, in particular, using neither electromagnetic simulation nor optimization. Thus, our method is amenable to be implemented using an off-the-shelf mathematical software tool such as Matlab. The proposed method is based on the synthesis of the heights and lengths of the final structure taking into account the shunt capacitance effect (produced by the excitation of higher-order modes at the height steps) over the magnitude and phase of the local reflection coefficients. The novel method has been validated with the design of a classical Chebyshev corrugated low-pass filter of order 31, which shows a remarkable accordance between the ideal and simulated frequency responses.
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
(2018)

Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximumlikelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.
Mid-term electricity price forecasting using SVM
(2016)

In the modern electricity market, it is very significant to have a precise electricity price forecasting in medium term time horizon. There are few studies done concentrating on the medium term forecasting of the electricity price. Medium term time horizon of electricity price forecasting is very useful and of great importance. It has many applications such as future maintenance scheduling of the power plants, risk management, plan future contracts, purchasing raw materials and determine the market pricing. To forecast the electricity price, some factors are very important, such as choosing the most useful price features that influence the market price, and choosing the proper prediction model that is able to predict the price variations behavior using the historical data. The proposed SVM method and the other employed methods are evaluated using the data from the New England ISO which are published at their official website.
Autonomous Intersection Management For Semi-Autonomous Vehicles
(2015)

Recent advances in autonomous vehicle technology will open the door to highly efficient transportation systems in the future, as demonstrated by Autonomous Intersection Management, an intersection control protocol designed for fully autonomous vehicles. We, however, anticipate there will be a long transition period during which most vehicles have some but not all capabilities of fully autonomous vehicles. This chapter introduces a new protocol called Semi-Autonomous Intersection Management, which allows vehicles with partiallyautonomous features such as adaptive cruise control to enter an intersection from different directions simultaneously. Our experiments show that this protocol can greatly decrease traffic delay when most vehicles are semi-autonomous. Our incremental deployment study reveals that traffic delay keeps decreasing as more and more vehicles employ features of autonomy.
SQL injection detection and prevention techniques
(2011)

As the use of internet is increasing rapidly, the attacks on web applications are also increasing as well. Nowadays SQL injection attack is a major issue of web applications. It allows unrestricted access to the database. The successful execution of SQL injection leads to a loss of integrity and confidentiality. In this paper, a review of different types of SQL injection attacks, their detection and prevention techniques are presented. This paper will help the researchers decide the technique of interest for further studies.
Autonomous Driving in Urban Environments: Boss and the Urban Challenge
(2008)

Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, M. N. Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matt McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod Snider, Anthony Stentz, William “Red” Whittaker, Ziv Wolkowicki, and Jason Ziglar Carnegie Mellon University Pittsburgh, Pennsylvania 15213 e-mail: curmson@ri.cmu.edu
Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction
(2010)

This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.
Model-Based Probabilistic Collision Detection in Autonomous Driving
(2009)

The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.
Digital color halftoning with generalized error diffusion and multichannel green-noise masks
(2000)

In this paper, we introduce two novel techniques for digital color halftoning with green-noise--stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.
Marrying Uncertainty and Time in Knowledge Graphs
(2017)

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs (KGs) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema. These facts are usually accompanied by a confidence score that witnesses how likely is for them to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availability of timewise knowledge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs. The goal of this paper is to fill this gap. We rely on two main ingredients. The first is a numerical extension of Markov Logic Networks (MLNs) that provide the necessary underpinning to formalize the syntax and semantics of uncertain temporal KGs. The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries for uncertain temporal KGs: maximum a-posteriori and conditional probability inference. Due to the hardness of these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches.
Business Models : A Discovery Driven Approach
(2010)

The business model concept offers strategists a fresh way to consider their options in uncertain, fast-moving and unpredictable environments. In contrast to conventional assumptions, recognizing that more new business models are both feasible and actionable than ever before is creating unprecedented opportunities for today’s organizations. However, unlike conventional strategies that emphasize analysis, strategies that aim to discover and exploit new models must engage in significant experimentation and learning e a ‘discovery driven,’ rather than analytical approach.
A Wideband Differential-Fed Slot Antenna Using Integrated Compact Balun With Matching Capability
(2014)

This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.
Cross-Point Resistive RAM Based on Field-Assisted Superlinear Threshold Selector
(2015)

We report a 3-D-stackable 1S1R passive cross-point resistive random access memory (RRAM). The sneak (leakage) current challenge in the cross-point RRAM integration has been overcome utilizing a field-assisted superlinear threshold selector. The selector offers high selectivity of >107, sharp switching slope of <;5 mV/decade, ability to tune the threshold voltage, stable operation at 125°C, and endurance of >1011. Furthermore, we demonstrate 1S1R integration in which the selector-subthreshold current is <;10 pA while offering >102 memory ON/OFF ratio and >106 selectivity during cycling. Combined with self-current-controlled RRAM, the 1S1R enables high-density and high-performance memory applications.
Augmented reality-based exergames for rehabilitation
(2016)

Rehabilitation for stroke afflicted patients, through exercises tailored for individual needs, aims at relearning basic motor skills, especially in the extremities. Rehabilitation through Augmented Reality (AR) based games engage and motivate patients to perform exercises which, otherwise, maybe boring and monotonic. Also, mirror therapy allows users to observe one's own movements in the game providing them with good visual feedback. This paper presents an augmented reality based system for rehabilitation by playing four interactive, cognitive and fun Exergames (exercise and gaming).
 The system uses low-cost RGB-D cameras such as Microsoft Kinect V2 to capture and generate 3D model of the person by extracting him/her from the entire captured data and immersing it in different interactive virtual environments. Animation based limb movement enhancement along with cognitive aspects incorporated in the game can help in positive reinforcement, progressive challenges and motion improvement. Recording module of the toolkit allows future reference and facilitates feedback from the physician. 10 able-bodied users, 2 psychological experts and 2 Physical Medicine and Rehabilitation physicians evaluated the user experience and usability aspects of the exergames. Results obtained shows the games to be fun and realistic, and at the same time engaging and motivating for performing exercises.
Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions
(2010)

In this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links.
An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images
(2014)

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.
Family issues in child anxiety: attachment, family functioning, parental rearing and beliefs.
(2006)

Family studies have found a large overlap between anxiety disorders in family members. In addition to genetic heritability, a range of family factors may also be involved in the intergenerational transmission of anxiety. Evidence for a relationship between family factors and childhood as well as parental anxiety is reviewed. Four groups of family variables are considered: (I) attachment; (II), aspects of family functioning, such as marital conflict, co-parenting, functioning of the family as a whole, and sibling relationships; (III) parental rearing strategies; and (IV) beliefs that parents hold about their child. The reviewed literature provides evidence for an association between each of these family factors and child anxiety. However, there is little evidence as yet that identified family factors are specific to child anxiety, rather than to child psychopathology in general. Moreover, evidence for a relationship between child anxiety and family factors is predominantly cross-sectional. Therefore, whether the identified family factors cause childhood anxiety still needs to be investigated. Further research that investigates mechanisms mediating the relationship between family factors and child anxiety is also called for. Finally, parental beliefs are identified as important predictors of parental behaviour that have largely not been investigated in relation to child anxiety disorders.